Staff Profile

Career Summary

Biography

The primary focus of my research is on correspondence analysis, in particular the case where categorical variables consist of ordinal responses; this is often the case in many social and scientific studies - such variable structures may form part of (for example) responses from questionnaires and surveys. Correspondence analysis is a statistical procedure (often linked to the family of multivariate data analysis techniques) that allows for a graphical summary of the association between two or more categorical variables. While investigating the theoretical properties of correspondence analysis, I have paid particular attention to ordinal contingency tables.

My work with ordinal categorical data also extends to partitioning quantities commonly used to measure the extent to which two (and more generally multiple) categorical variables are associated. In particular, such partitions have been applied to the Pearson chi-squared statistic (for symmetrically related variables) and the Goodman-Kruskal tau index, Marcotorchino index and the Gray-William indices (all used for asymmetrically associated variables).

Other work I have undertaken, in collaboration with my colleagues, has resulted in an iterative free (direct) procedure for estimating the linear-by-linear association term commonly included in ordinal log-linear models. Work is currently being developed to look at the properties of these direct procedures and their extension to more general cases. I have also been very interested in ecological inference - a technique used to identify the behaviour of individual/cellular level data when only aggregate data is available. In particular, I am interested in the properties of a single 2x2 contingency table where the cells of the table are unknown or missing

Qualifications

Doctor of Philosophy, University of Wollongong, 12/05/1999

Bachelor of Mathematics (Honours), University of Wollongong, 17/05/1995

Research

Research keywords

Categorical Data Analysis

Correspondence Analysis

Ecological Inference

Measures of Association

Statistics

Research expertise

My research interests cover the broad statistical topic of categorical data analysis, with particular emphasis on the mathematical development and application of correspondence analysis, measures of bivariate and multivariate association, log-linear models and ecological inference.

The primary focus of my research is on correspondence analysis, in particular the case where categorical variables consist of ordinal responses; this is often the case in many social and scientific studies - such variable structures may form part of (for example) responses from questionnaires and surveys. Correspondence analysis is a statistical procedure (often linked to the family of multivariate data analysis techniques) that allows for a graphical summary of the association between two or more categorical variables. While investigating the theoretical properties of correspondence analysis, I have paid particular attention to ordinal contingency tables.

My work with ordinal categorical data also extends to partitioning quantities commonly used to measure the extent to which two (and more generally multiple) categorical variables are associated. In particular, such partitions have been applied to the Pearson chi-squared statistic (for symmetrically related variables) and the Goodman-Kruskal tau index, Marcotorchino index and the Gray-William indices (all used for asymmetrically associated variables).

Other work I have undertaken, in collaboration with my colleagues, has resulted in an iterative free (direct) procedure for estimating the linear-by-linear association term commonly included in ordinal log-linear models. Work is currently being developed to look at the properties of these direct procedures and their extension to more general cases. I have also been very interested in ecological inference - a technique used to identify the behaviour of individual/cellular level data when only aggregate data is available. In particular, I am interested in the properties of a single 2x2 contingency table where the cells of the table are unknown or missing.

Fields of Research

Code

Description

Percentage

010499

Statistics Not Elsewhere Classified

70

080299

Computation Theory And Mathematics Not Elsewhere Classified

15

140399

Econometrics Not Elsewhere Classified

15

Collaboration

My research interests cover the broad statistical topic of categorical data analysis, with particular emphasis on the mathematical development and application of correspondence analysis, measures of bivariate and multivariate association, log-linear models and ecological inference. I am actively engaged in collaborations with national and international researchers including

Professor Luigi D'Ambra, University of Naples, Italy

Professor Thomas B. Farver, University of California - Davis, USA

Associate Professor Rosaria Lombardo, Second University of Naples, Italy

Conference (23 outputs)

Year

Citation

Altmetrics

Link

2014

Tran D, Beh EJ, Hudson I, 'Generalising the aggregate association index: Its connection with the odds ratio and other common association measurements', Abstracts of Australian Statistical Conference in conjunction with the Institute of Mathematical Statistics Annual Meeting, Sydney (2014) [E3]

Cheema S, Beh EJ, Hudson I, 'Adjustment to the aggregate association index for large samples', Abstracts of the Australian Statistical Conference in conjunction with the Institute of Mathematical Statistics Annual Meeting, Sydney (2014) [E3]

Hudson IL, Tran D, Beh EJ, 'The Aggregate Association Index and its links with common measurements of association in a 2x2 table: An Analysis of Early NZ gendered voting data', MODSIM2013, 20th International Congress on Modelling and Simulation, Adelaide, South Australia (2013) [E1]